mlsanityData to 7 Oct 2026

ToTTo: A Controlled Table-To-Text Generation Dataset

arXiv 2004.14373 ↗First posted 29 Apr 2020cs.CL cs.LGcited by 267

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Cited by 21 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We present ToTTo, an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description. To obtain generated targets that are natural but also faithful to the source table, we introduce a dataset construction process where annotators directly revise existing candidate sentences from Wikipedia. We present systematic analyses of our dataset and annotation process as well as results achieved by several state-of-the-art baselines. While usually fluent, existing methods often hallucinate phrases that are not supported by the table, suggesting that this dataset can serve as a useful research benchmark for high-precision conditional text generation.

Key facts. Cited by 267 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Apr 2020.

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Citing papers per year: 2021: 37; 2022: 42; 2023: 64; 2024: 48; 2025: 49; 2026: 27.

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